A Century of Portraits: A Visual Historical Record of American High School Yearbooks
| Autor: | Alexei A. Efros, Kate Rakelly, Crystal Lee, Shiry Ginosar, Sarah Sachs, Brian Yin, Philipp Krähenbühl |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Data science Computer Science Applications Visualization Visual arts Computational Mathematics Portrait Signal Processing Visual patterns 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Yearbook Treasure Classifier (UML) Historical record Visual culture |
| Zdroj: | IEEE Transactions on Computational Imaging. 3:421-431 |
| ISSN: | 2334-0118 |
| Popis: | Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-increasing number of photographs document our "visual culture" complementing historical texts. But currently, this treasure trove of knowledge can only be analyzed manually by historians, and only at small scale. In this paper we perform automated analysis on a large-scale historical image dataset. Our main contributions are: 1) A publicly-available dataset of 168,055 (37,921 frontal-facing) American high school yearbook portraits. 2) Weakly-supervised data-driven techniques to discover historical visual trends in fashion and identify date-specific visual patterns. 3) A classifier to predict when a portrait was taken, with median error of 4 years for women and 6 for men. 4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era. Project page can be found at: http://people.eecs.berkeley.edu/~shiry/projects/yearbooks/yearbooks.html . Comment: IEEE Transactions on Computational Imaging, September 2017 |
| Databáze: | OpenAIRE |
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